Description

After exploring the general pattern of modelling GPP vs observational GPP, the next step to identify the specific period when the mismatch between modeled GPP and observed GPP in each site–>focused in the markdown file

step1: tidy the table for GPP simulation vs GPP obs sites

step2: finding the way to separate out the model early simulation period

step1: tidy the table

library(kableExtra)
library("readxl")
table.path<-"D:/CES/Data_for_use/Merge_Data/ECflux_and_PhenoCam_site_info/"
# my_data <- read.csv(paste0(table.path,"ECflux_and_PhenoCam_site_info_add_manually.csv"))
#after updating the information, now new updated information is:
my_data <- read.csv(paste0(table.path,"ECflux_and_PhenoCam_site_info_add_manually_final.csv"))
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
#   kable_paper(full_width = F, html_font = "Cambria") %>%
#   scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
  kbl(caption = "Summary of sites with GPP estimation beyond Beni's datasets") %>%
  kable_classic(full_width = F, html_font = "Cambria")
Summary of sites with GPP estimation beyond Beni’s datasets
SiteName Site_years Site_fullme Lat. Long. ELV. PFT MAT MAP Clim. Site_flag Delay_status Period N Calib. Avai.alyzed.years.spring Avai.site.years.spring Avai.alyzed.years.springawinter Avai.site.years.springawinter Reference PhenoCam Cam_Period Cam_Source Cam_sitename Cam_Avai
AT-Neu 10.926027 Neustift 47.1167 11.3175 970 GRA 6.50 852.00 Dfc Beyond 2002-2012 NA 2002-2012 NA 2002-2012 NA Y 2012-2021 PhenoCam USA innsbruck Y
BE-Bra 14.175342 Brasschaat 51.3076 4.5198 16 MF 9.80 750.00 Cfb Beyond no obs data NA no obs data NA no obs data NA Y 2009-2014 EuroPhen Brasschaat N
CA-NS1 3.131507 UCI-1850 burn site 55.8792 -98.4839 260 ENF -2.89 500.29 Dfc Beyond 2002-2005 NA 2003-2005 NA 2003-2004 NA N
CA-NS3 4.142466 UCI-1964 burn site 55.9117 -98.3822 260 ENF -2.87 502.22 Dfc Beyond 2001-2005 NA 2002-2005 NA 2002-2004 NA N
CH-Fru 8.520548 Frebel 47.1158 8.5378 982 GRA 7.20 1651.00 Dfb Beyond 2005-2014 NA 2006-2014 NA 2007-2008, 2010-2014 NA Y 2008-2014 EuroPhen Früebüel N
CN-Qia 3.000000 Qianyanzhou 26.7414 115.0581 NA ENF 18.95 1466.75 Cfa Beyond 2003-2006 NA 2003-2006 NA 2003-2006 NA N
CZ-BK1 10.167123 Bily Kriz forest 49.5021 18.5369 875 ENF 6.70 1316.00 Dfb Beyond 2004-2008 NA 2004-2008 NA 2004-2008 NA N
CZ-BK2 5.816438 Bily Kriz grassland 49.4944 18.5429 855 GRA 6.70 1316.00 Dfb Beyond 2006-2007 NA lack early doy NA no years NA N
DE-Lkb 3.967123 Lackenberg 49.0996 13.3047 1308 ENF 4.00 1599.00 Dfc Beyond 2009-2013 NA 2010-2013 NA 2010-2013 NA N
DK-Sor 17.742466 Soroe 55.4859 11.6446 40 DBF 8.20 660.00 Dfb Beyond 2000-2014 NA 2000-2003,2005-2013 NA 2000-2003,2005-2013 NA Y 2009-2014 EuroPhen Sorø Y
FR-Fon 9.424658 Fontainebleau-Barbeau 48.4764 2.7801 103 DBF 10.20 720.00 Cfb Beyond 2005-2014 NA 2006-2013 NA 2006-2013 NA Y 2012-2014 EuroPhen Fontainebleau N
FR-LBr 10.509589 Le Bray 44.7171 -0.7693 61 ENF 13.60 900.00 Cfb Beyond 2000-2008 NA 2001, 2004-2008 NA 2001, 2004-2008 NA N
IT-Col 13.315069 Collelongo 41.8494 13.5881 1560 DBF 6.30 1180.00 Dfb Beyond 2000-2014 NA 2001,2005,2007-2011,2013-2014 NA 2001,2005,2007-2011,2013-2014 NA N
IT-Lav 11.679452 Lavarone 45.9562 11.2813 1353 ENF 7.80 1291.00 Dfb Beyond 2003-2014 NA 2003-2014 NA 2003-2014 NA N
IT-MBo 10.854795 Monte Bondone 46.0147 11.0458 1550 GRA 5.10 1214.00 Dfb Beyond 2003-2013 NA 2003-2013 NA 2003-2013 NA Y 2015-2021 PhenoCam USA montebondonegrass Y
JP-SMF 3.920548 Seto Mixed Forest Site 35.2617 137.0788 NA MF NA NA Cfa Beyond 2002-2006 NA 2003-2006 NA 2003,2005-2007 NA N
RU-Fyo 15.465753 Fyodorovskoye 56.4615 32.9221 265 ENF 3.90 711.00 Dfb Beyond 2000-2014 NA 2000-2001,2003-2014 NA 2000-2001,2003-2014 NA N
US-AR1 3.386301 ARM USDA UNL OSU Woodward Switchgrass 1 36.4267 -99.4200 611 GRA NA NA Cfa Beyond 2009-2012 NA bad quality NA NA N
US-AR2 3.106849 ARM USDA UNL OSU Woodward Switchgrass 2 36.6358 -99.5975 646 GRA NA NA Cfa Beyond 2009-2012 NA bad quality NA NA N
US-GLE 9.293151 GLEES 41.3665 -106.2399 3197 ENF 0.80 1200.00 Dfc Beyond 2004-2014 NA 2006-2014 NA 2006-2014 NA N
US-Ha1 20.115068 Harvard Forest EMS Tower (HFR1) 42.5378 -72.1715 340 DBF 6.62 1071.00 Dfb Beyond 2000-2012 NA 2000-2012 NA 2000-2012 NA Y 2015-2021 PhenoCam USA bbc1-bbc2 Y
US-MMS 15.619178 Morgan Monroe State Forest 39.3232 -86.4131 275 DBF 10.85 1032.00 Dfa Beyond 2000-2014 NA 2000-2014 NA 2000-2014 NA Y 2008-2021 PhenoCam USA morganmonroe Y
US-NR1 15.627397 Niwot Ridge Forest (LTER NWT1) 40.0329 -105.5464 3050 ENF 1.50 800.00 Dfc Beyond 2000-2014 NA 2000-2014 NA 2000-2014 NA Y 2009-2015 PhenoCam USA niwot2 Y
US-PFa 17.704110 Park Falls/WLEF 45.9459 -90.2723 470 MF 4.33 823.00 Dfb Beyond 2000-2014 NA 2000-2004,2006-2014 NA NA N
US-Prr 3.852055 Poker Flat Research Range Black Spruce Forest 65.1237 -147.4876 210 ENF -2.00 275.00 Dfc Beyond 2010-2013 NA 2010-2012 NA 2011 NA N
DE-Hai NA 51.0800 10.4500 NA DBF NA NA Cfb Beni Yes 2000-2012 4247 Y 2000-2012 13 2000-2012 13 Knohl et al. (2003) Y 2003-2014 EuroPhen Y
US-Syv NA 46.2400 -89.3500 NA MF NA NA Dfb Beni Yes 2001-2014 2635 Y 2002-2006, 2014 6 2002, 2004-2006,2014 5 Desai et al. (2005) Y 2015-2021 PhenoCam USA sylvania Y
US-UMB NA 45.5600 -84.7100 NA DBF NA NA Dfb Beni Yes 2000-2014 4015 Y 2000-2014 15 2000-2014 15 Gough et al. (2013) Y 2008-2021 PhenoCam USA umichbiological Y
US-UMd NA 45.5600 -84.7000 NA DBF NA NA Dfb Beni Yes 2007-2014 2050 Y 2008-2014 7 2008-2013 6 Gough et al. (2013) Y 2008-2021 PhenoCam USA umichbiological2 Y
US-WCr NA 45.8100 -90.0800 NA DBF NA NA Dfb Beni Yes 1999-2014 3425 Y 2000-2006, 2011-2014 11 2000-2006, 2011-2014 11 Cook et al. (2004) Y 2011-2021 PhenoCam USA willowcreek Y
CA-Man NA 55.8800 -98.4800 NA ENF NA NA Dfc Beni Yes 1994-2008 1910 2000-2003, 2007-2008 6 2000-2003, 2007 5 Dunn et al. (2007) N
CA-NS2 NA 55.9100 -98.5200 NA ENF NA NA Dfc Beni Yes 2001-2005 1123 2002, 2004 (2003 lack early doy) 2 2002 1 N
CA-NS4 NA 55.9100 -98.3800 NA ENF NA NA Dfc Beni Yes 2002-2005 756 2005 (2003 lack early doy) 1 no years 0 N
CA-NS5 NA 55.8600 -98.4800 NA ENF NA NA Dfc Beni Yes 2001-2005 1245 2002, 2004-2005 (2003 lack early doy) 3 2002, 2004 2 N
CA-Qfo NA 49.6900 -74.3400 NA ENF NA NA Dfc Beni Yes 2003-2010 2416 2004-2010 7 2004-2010 7 Bergeron et al. (2007) Y 2008-2011 PhenoCam USA chibougamau Y
FI-Hyy NA 61.8500 24.3000 NA ENF NA NA Dfc Beni Yes 1996-2014 4587 Y 2000-2014 15 2000-2004, 2006-2014 14 Suni et al. (2003) Y 2008-2014 EuroPhen Y
IT-Tor NA 45.8400 7.5800 NA GRA NA NA Dfc Beni Yes 2008-2014 2172 Y 2009-2014 6 2009-2014 6 Galvagno et al. (2013) Y 2009-2021 PhenoCam USA torgnon-nd Y
IT-Ren NA 46.5900 11.4300 NA ENF NA NA Dfc Beni No 1998-2013 3405 Y 2002-2003,2005-2013 11 2002-2003,2005-2013 11 Montagni et al. (2009) N
BE-Vie NA 50.3100 6.0000 NA MF NA NA Cfb Beni No 1996-2014 4910 Y 2000-2014 15 2000-2014 15 Aubinet et al. (2001) Y 2010-2014 EuroPhen Y
CH-Cha NA 47.2100 8.4100 NA GRA NA NA Cfb Beni No 2005-2014 2944 2006-2008,2010-2014 8 2006-2008,2010-2014 8 Merbold et al. (2014) N
CH-Lae NA 47.4800 8.3700 NA MF NA NA Cfb Beni No 2004-2014 3551 Y 2005-2014(2004 lack early doy) 10 2005-2014(2004 lack early doy) 10 Etzold et al. (2011) Y 2010-2014 EuroPhen Y
CH-Oe1 NA 47.2900 7.7300 NA GRA NA NA Cfb Beni No 2002-2008 2184 Y 2002-2008 7 2002-2008 7 Ammann et al. (2009) N
DE-Gri NA 50.9500 13.5100 NA GRA NA NA Cfb Beni No 2004-2014 3642 Y 2004-2014 11 2004-2014 11 Prescher et al. (2010) Y 2007-2014 EuroPhen N
DE-Obe NA 50.7800 13.7200 NA ENF NA NA Cfb Beni No 2008-2014 2260 Y 2008-2014 7 2008-2014 7 N
DE-RuR NA 50.6200 6.3000 NA GRA NA NA Cfb Beni No 2011-2014 1227 Y 2012-2014 3 2012-2014 3 Post et al. (2015) N
DE-Tha NA 50.9600 13.5700 NA ENF NA NA Cfb Beni No 1996-2014 5141 Y 2000-2014 15 2000-2014 15 Grünwald and Bernhofer (2007) Y 2009-2014 EuroPhen Y
NL-Hor NA 52.2400 5.0700 NA GRA NA NA Cfb Beni No 2004-2011 2188 Y 2005,2007-2011 6 2005,2007-2010 5 Jacobs et al. (2007) N
NL-Loo NA 52.1700 5.7400 NA ENF NA NA Cfb Beni No 1996-2013 4671 Y 2000-2013 14 2000-2013 14 Moors (2012) N

step2: seprate the time period when model early estimation of GPP

Part1: find the method to determine the period that with early GPP estimation

Part 2: check all the sites

## [1] 11

(1) For Cfa:both for MF and ENF sites - Cfa-MF (1 site)

## [1] 4

- Cfa-ENF (1 site)

## [1] 3

(2) For Cfb: for DBF and ENF - Cfb-DBF (1 site)

## [1] 8

  • Cfb-ENF (1 site)
## [1] 6

(3) For Dfa: for DBF - Dfa-DBF (1 site)

## [1] 15

(3) For Dfb: for GRA, DBF, MF and ENF - Dfb-GRA (2 sites)

## [1] 9

## [1] 11

  • Dfb-DBF (3 sites)
## [1] 13

## [1] 9

## [1] 13

- Dfb-MF (1 sites)

## [1] 14

- Dfb-ENF (3 sites)

## [1] 5

## [1] 12

## [1] 14

(4) For Dfc:both for GRA and ENF sites

  • Dfc-GRA (1 site)
## [1] 11

  • Dfc-ENF (6 sites)
## [1] 3

## [1] 4

## [1] 4

## [1] 9

## [1] 15

## [1] 3

##working to here–>08-26 ## step3: save the data that label with “is_event”

Summary

steps to determine the “is_event” period

Step1: normlization for all the years in one site

#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)

Step 2:Determine the green-up period for each year(using spline smoothed values):

#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)

  • using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period

    • update in Aug,31,2011–>limit the sos late than Feburary(Doy:60)–>in order to remove some unrelastic sos

    Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)

    also for the data beyond green-up period–> the code of this steps moves to second step

    • at the end, I select the 20 days windows for the rolling mean

    Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period

    • The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).

    • But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:

      1. [peak,265/366]

      2. DoY[1, sos]& DOY[peak,365/366]

      3. [1,sos] & [eos,365/366]

    I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.

    step 5:determine the “is_event” within green-up period

    • After some time of consideration, I took following crition to determine the “is_event”:

      1. during the green-up period (sos,peak)–>the data with GPP biases bigger than 3 SD are classified as the “GPP overestimation points”

      2. For “GPP overestimation points” –> only regard the data points in the first 2/3 green-up period as the “is_event”

      3. For “is_event points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event_less10”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)

      References:

      Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052

      Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108

    step 6:Evaluation “is_event”–>visualization and stats

    • two ways to evaluate if “is_event” is properly determined:

    1. visulization

    2. stats: \[ Pfalse = /frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]